Asymptotic universal moment matching properties of normal distributions

TL;DR

This paper characterizes when moment matching reduces variance asymptotically in Monte Carlo simulations, showing it is guaranteed only for normal distributions and proposing schemes for other distributions.

Contribution

It establishes that asymptotic variance reduction via moment matching occurs only with normal distributions and provides formulas and schemes for variance estimation and reduction.

Findings

Asymptotic variance reduction is guaranteed only for normal distributions.

Formulas for efficient variance estimation in normal cases are derived.

Non-linear moment matching schemes can ensure variance reduction for any continuous distribution.

Abstract

Moment matching is an easy-to-implement and usually effective method to reduce variance of Monte Carlo simulation estimates. On the other hand, there is no guarantee that moment matching will always reduce simulation variance for general integration problems at least asymptotically, i.e. when the number of samples is large. We study the characterization of conditions on a given underlying distribution $X$ under which asymptotic variance reduction is guaranteed for a general integration problem $\mathbb{E}[f(X)]$ when moment matching techniques are applied. We show that a sufficient and necessary condition for such asymptotic variance reduction property is $X$ being a normal distribution. Moreover, when $X$ is a normal distribution, formulae for efficient estimation of simulation variance for (first and second order) moment matching Monte Carlo are obtained. These formulae allow estimations of simulation variance as by-products of the simulation process, in a way similar to variance estimations for plain Monte Carlo. Moreover, we propose non-linear moment matching schemes for any given continuous distribution such that asymptotic variance reduction is guaranteed.